Technologies for hardware assisted native malware detection

ABSTRACT

Technologies for hardware assisted native malware detection include a computing device. The computing device includes one or more processors with hook logic to monitor for execution of branch instructions of an application, compare the monitored branch instructions to filter criteria, and determine whether a monitored branch instruction satisfies the filter criteria. Additionally, the computing device includes a malware detector to provide the filter criteria to the hook logic, provide an address of a callback function to the hook logic to be executed in response to a determination that a monitored branch instruction satisfies the filter criteria, and analyze, in response to execution of the callback function, the monitored branch instruction to determine whether the monitored branch instruction is indicative of malware. Other embodiments are also described and claimed.

BACKGROUND

Virtualization-based malware analysis sandboxes have become one of themainstream methods to inspect malicious behavior before an unknownobject, such as an application, is allowed to continue to be processedon a computing device. The reason is that virtualization presentscapabilities such as invisible memory monitoring and disk input/output(IO) virtualization. However, advanced malware that uses evasiontechniques can detect that it is within a virtualization-based sandboxsystem and, in response, hide or modify its intended malicious behavior.

Some systems use native environments to prevent virtualization-awaremalware from modifying its behavior and avoiding detection. However,these native approaches suffer from other issues, such as loss ofdetection capabilities, derived from the fact that the native systemdoes not run a hypervisor. Further, these systems typically havesignificant complexity and delays attributable to disk image managementand system restoration operations. In a native system, monitoring thebehavior of an application to detect malware relies on setting hooks inthe code of the application. This typically requires recompilation ofthe code and advanced malware is able to detect the hooks added to theapplication. In response, the malware may adjust its behavior to avoiddetection.

BRIEF DESCRIPTION OF THE DRAWINGS

The concepts described herein are illustrated by way of example and notby way of limitation in the accompanying figures. For simplicity andclarity of illustration, elements illustrated in the figures are notnecessarily drawn to scale. Where considered appropriate, referencelabels have been repeated among the figures to indicate corresponding oranalogous elements.

FIG. 1 is a simplified block diagram of at least one embodiment of asystem for performing hardware assisted native malware detection, inwhich a computing device is coupled to a server;

FIG. 2 is a simplified block diagram of at least one embodiment of anenvironment that may be established by the computing device of FIG. 1;

FIGS. 3-4 are a simplified flow diagram of at least one embodiment of amethod for configuring the computing device of FIG. 1 to detect malware;and

FIGS. 5-6 are a simplified flow diagram of at least one embodiment of amethod for monitoring operations to detect malware that may be performedby the computing device of FIG. 1.

DETAILED DESCRIPTION OF THE DRAWINGS

While the concepts of the present disclosure are susceptible to variousmodifications and alternative forms, specific embodiments thereof havebeen shown by way of example in the drawings and will be describedherein in detail. It should be understood, however, that there is nointent to limit the concepts of the present disclosure to the particularforms disclosed, but on the contrary, the intention is to cover allmodifications, equivalents, and alternatives consistent with the presentdisclosure and the appended claims.

References in the specification to “one embodiment,” “an embodiment,”“an illustrative embodiment,” etc., indicate that the embodimentdescribed may include a particular feature, structure, orcharacteristic, but every embodiment may or may not necessarily includethat particular feature, structure, or characteristic. Moreover, suchphrases are not necessarily referring to the same embodiment. Further,when a particular feature, structure, or characteristic is described inconnection with an embodiment, it is submitted that it is within theknowledge of one skilled in the art to effect such feature, structure,or characteristic in connection with other embodiments whether or notexplicitly described. Additionally, it should be appreciated that itemsincluded in a list in the form of “at least one A, B, and C” can mean(A); (B); (C); (A and B); (A and C); (B and C); or (A, B, and C).Similarly, items listed in the form of “at least one of A, B, or C” canmean (A); (B); (C); (A and B); (A and C); (B and C); or (A, B, and C).

The disclosed embodiments may be implemented, in some cases, inhardware, firmware, software, or any combination thereof. The disclosedembodiments may also be implemented as instructions carried by or storedon a transitory or non-transitory machine-readable (e.g.,computer-readable) storage medium, which may be read and executed by oneor more processors. A machine-readable storage medium may be embodied asany storage device, mechanism, or other physical structure for storingor transmitting information in a form readable by a machine (e.g., avolatile or non-volatile memory, a media disc, or other media device).

In the drawings, some structural or method features may be shown inspecific arrangements and/or orderings. However, it should beappreciated that such specific arrangements and/or orderings may not berequired. Rather, in some embodiments, such features may be arranged ina different manner and/or order than shown in the illustrative figures.Additionally, the inclusion of a structural or method feature in aparticular figure is not meant to imply that such feature is required inall embodiments and, in some embodiments, may not be included or may becombined with other features.

Referring now to FIG. 1, in an illustrative embodiment, a system 100 forperforming hardware assisted native malware detection includes acomputing device 110 in communication with a server 150 through anetwork 140. The computing device 110 may receive, from the server 150,one or more applications that may contain malware. As such, as explainedin more detail herein, the computing device 110 may perform a hardwareassisted malware detection process to efficiently detect malware withoutproviding an indication to the malware that the computing device 110 isperforming the detection process. In the illustrative embodiment, thecomputing device 110 does this by using specialized logic in the centralprocessing unit (CPU) to set an “invisible hook” to analyze branchinstructions from one or more applications, and to use specialized logicin a data storage device to redirect any data write operations to asection (a “shadow area”) of the data storage device that is separatefrom an area that contains a clean, unmodified system image. In responseto detecting malware, the computing device 110 may restart and recoverfrom the unmodified system image, to protect itself from any maliciousdata that may have been written by the malware.

The computing device 110 may be embodied as any type of computing devicecapable of performing the functions described herein. For example, insome embodiments, the computing device 110 may be embodied as, withoutlimitation, a computer, a smartphone, a tablet computer, a consumerelectronic device, a smart appliance, a laptop computer, a notebookcomputer, a desktop computer, and/or any other computing device capableof performing functions to perform a hardware assisted detection ofmalware, as described herein. As shown in FIG. 1, the illustrativecomputing device 110 includes a central processing unit (CPU) 112, amain memory 116, an input/output subsystem 118, a data storage device120, a display 124, and communication circuitry 126. Of course, thecomputing device 110 may include other or additional components, such asthose commonly found in a computer (e.g., one or more peripheraldevices, etc.). Additionally, in some embodiments, one or more of theillustrative components may be incorporated in, or otherwise from aportion of, another component. For example, the memory 116, or portionsthereof, may be incorporated in the CPU 112 in some embodiments.

The CPU 112 may be embodied as any type of processing device capable ofperforming the functions described herein. For example, the CPU 112 maybe embodied as a single or multi-core processor(s) having one or moreprocessor cores, a microcontroller, or other processor orprocessing/controlling circuit. In the illustrative embodiment, the CPU112 includes hook logic 114, which may be any hardware componentscapable of monitoring instructions (i.e., opcodes) to be executed by theCPU 112 on behalf of an application or other process (i.e., potentialmalware), comparing the monitored instructions to a set of filtercriteria to determine whether an instruction matches the filtercriteria, and if so, executing a callback function located at aspecified address to analyze the instruction in greater detail. Ineffect, the hook logic 114 provides the ability to set an inline hookinto an application or process without actually adding inline hook codeto the application or process. As such, the inline hook functionalityprovided by the hook logic 114 is invisible to the malware will notcause the malware to modify its behavior to avoid detection. Further,given that the hook logic 114 is implemented in specialized hardwarethat operates alongside the standard components of the CPU 112, the CPU112 may execute the monitored applications or processes at a normalspeed, without incurring a performance penalty.

The main memory 116 may be embodied as any type of volatile ornon-volatile memory or data storage capable of performing the functionsdescribed herein. In operation, the main memory 116 may store variousdata and software used during operation of the computing device 110 suchas branch filters, malware signatures, operating systems, applications,programs, libraries, and drivers. The main memory 116 is communicativelycoupled to the CPU 112 via the I/O subsystem 118. Of course, in otherembodiments (e.g., those in which the CPU 112 includes a memorycontroller), the main memory 116 may be directly communicatively coupledto the CPU 112.

The I/O subsystem 118 may be embodied as circuitry and/or components tofacilitate input/output operations with the CPU 112, the main memory116, and other components of the computing device 110. For example, theI/O subsystem 118 may be embodied as, or otherwise include, memorycontroller hubs, input/output control hubs, firmware devices,communication links (i.e., point-to-point links, bus links, wires,cables, light guides, printed circuit board traces, etc.) and/or othercomponents and subsystems to facilitate the input/output operations. Insome embodiments, the I/O subsystem 118 may form a portion of asystem-on-a-chip (SoC) and be incorporated, along with the CPU 112, thememory 116, and other components of the computing device 110, on asingle integrated circuit chip.

The data storage device 120 may be embodied as any type of device ordevices configured for short-term or long-term storage of data such as,for example, memory devices and circuits, memory cards, solid-statedrives, hard disk drives, or other data storage devices. In theillustrative embodiment, the data storage device 120 is a solid-statedrive. Further, in the illustrative embodiment, the data storage device120 includes checkpoint logic 122. The checkpoint logic 122, may beembodied as any hardware and/or firmware components capable ofmaintaining a system image (a “golden image”) that includes theoperating system, applications, and other data of the computing device110 at a particular point in time, such as when the computing device 110was configured by an original equipment manufacturer (OEM). Thecheckpoint logic 122 is also capable of performing a recovery operationto return the computing device 110 to its state at that earlier point intime using the system image. Additionally, in the illustrativeembodiment, the checkpoint logic 122 is capable of selectivelyredirecting data write operations to a separate section of storage,referred to as a “shadow area”, to avoid modifying the system image. Inaddition, the checkpoint logic 122 is capable of determining differencesbetween the shadow area and the system image to assist in analyzing howone or more applications or processes may have attempted to modify theoperating system files or other aspects of the system image. Given thatthe illustrative checkpoint logic 122 is embodied in hardware, thesefunctions are offloaded from the CPU 112 and may be performed moreefficiently than if they were implemented in software.

The display 124 may be embodied as, or otherwise use, any suitabledisplay technology including, for example, a liquid crystal display(LCD), a light emitting diode (LED) display, a cathode ray tube (CRT)display, a plasma display, and/or other display usable in a computedevice. The display may include a touchscreen sensor that uses anysuitable touchscreen input technology to detect the user's tactileselection of information displayed on the display including, but notlimited to, resistive touchscreen sensors, capacitive touchscreensensors, surface acoustic wave (SAW) touchscreen sensors, infraredtouchscreen sensors, optical imaging touchscreen sensors, acoustictouchscreen sensors, and/or other type of touchscreen sensors. Asdescribed herein, the display 124 may be used receive filter criteriafor malware, to report the details of a malware detection to a user,and/or to display or receive other information from a user.

The communication circuitry 126 may be embodied as any communicationcircuit, device, or collection thereof, capable of enablingcommunications over the network 140, such as between the computingdevice 110 and the server 150. The communication circuitry 126 may beconfigured to use any one or more communication technology (e.g., wiredor wireless communications) and associated protocols (e.g., Ethernet,Bluetooth®, Wi-Fi®, WiMAX, etc.) to effect such communication.

The illustrative communication circuitry 126 includes a networkinterface controller (NIC) 128, which may also be referred to as a hostfabric interface (HFI). The NIC 128 may be embodied as one or moreadd-in-boards, daughtercards, network interface cards, controller chips,chipsets, or other devices that may be used by the computing device 110to connect with other computing devices, such as the server 150. In someembodiments, the NIC 128 may be embodied as part of a system-on-a-chip(SoC) that includes one or more processors, or included on a multichippackage that also contains one or more processors. In some embodiments,the NIC 128 may include a local processor (not shown) and/or a localmemory (not shown) that are both local to the NIC 128.

Additionally or alternatively, the computing device 110 may include oneor more peripheral devices 130. Such peripheral devices 130 may includeany type of peripheral device commonly found in a computing device suchas speakers, a mouse, a keyboard, and/or other input/output devices,interface devices, and/or other peripheral devices.

Still referring to FIG. 1, the server 150 may be embodied as any type ofserver computer capable of providing one or more applications to thecomputing device 110, such as a web server, a file transfer protocol(FTP) server, or other type of server. The server 150 may includecomponents commonly found in a server computer, such as a processor,memory, I/O subsystem, data storage, communication subsystem, etc. Thosecomponents may be substantially similar to the corresponding componentsof the computing device 110, without the hook logic 114 and thecheckpoint logic 122. As such, further descriptions of the likecomponents are not repeated herein with the understanding that thedescription of the corresponding components provided above in regard tothe computing device 110 applies equally to the corresponding componentsof the server 150.

Still referring to FIG. 1, the network 140 may be embodied as any numberof various wireless or wired networks. For example, the network 140 maybe embodied as, or otherwise include, a publicly-accessible, globalnetwork such as the Internet, a cellular network, a wireless or wiredwide area network (WAN), or a wireless or wired local area network(LAN). As such, the network 140 may include any number of additionaldevices, such as additional computers, routers, and switches, tofacilitate communications among the devices of the system 100.

Referring now to FIG. 2, in the illustrative embodiment, the computingdevice 110 may establish an environment 200 during operation. Theillustrative environment 200 includes a malware detector 210, a storagemanager 220, and a network communicator 230. Each of the components ofthe environment 200 may be embodied as hardware, firmware, software, ora combination thereof. As such, in some embodiments, one or more of thecomponents of the environment 200 may be embodied as circuitry or acollection of electrical devices (e.g., malware detection circuitry 210,storage management circuitry 220, network communication circuitry 230,etc.). It should be appreciated that, in such embodiments, one or moreof the malware detection circuitry 210, storage management circuitry220, and network communication circuitry 230 may form a portion of oneor more of the CPU 112, data storage device 120, main memory 116,communication circuitry 126 and/or other components of the computingdevice 110. Additionally, in some embodiments, one or more of theillustrative components may form a portion of another component and/orone or more of the illustrative components may be independent of oneanother. Further, in some embodiments, one or more of the components ofthe environment 200 may be embodied as virtualized hardware componentsor emulated architecture, which may be established and maintained by theCPU 112 or other components of the computing device 110.

In the illustrative environment 200, the computing device 110 alsoincludes branch filters 202, malware signatures 204, an unmodified image206, and shadow data 208. In the illustrative embodiment, the branchfilters 202 are embodied as or include data representing criteria forfiltering monitored branch instructions down to those that may warrantfurther analysis for malware detection, such as calls to applicationprogramming interface (API) functions of the operating system. Themalware signatures 204 may be embodied as or include data indicative ofknown operations or behaviors of malware, including files that malwareis known to modify, memory areas that malware is known to write to,and/or other known operations or behaviors of malware. As described inmore detail herein, the computing device 110, may analyze a branchinstruction satisfying the branch filter 202 to determine whether thebranch instruction is indicative of one or more of the malwaresignatures 204. Further, as described in more detail herein, thecomputing device 110 may analyze write operations that were redirectedto the shadow area to identify additional malware behavior data and addthe behavior data to the malware signatures 204. The unmodified image206 may be embodied as or include an image of the operating system,applications, and other data of the computing device 110 at a particularpoint in time, such as when the computing device 110 was configured byan original equipment manufacturer (OEM), and is usable to return thecomputing device 110 to its state at that particular point in time. Inthe illustrative embodiment, the shadow data 208 is embodied as orincludes data from write operations that have been selectivelyredirected to the shadow area of the data storage device 120. As such,the shadow data 208 may include data that was intended by malware tooverwrite system files, such as operating system files, and/or otherdata written during the operation of computing device 110. The branchfilters 202, malware signatures 204, unmodified image 206, and shadowdata 208 may be accessed by the various components and/or sub-componentsof the computing device 110. It should be appreciated that the computingdevice 110 may include other components, sub-components, modules,sub-modules, and/or devices commonly found in a computing device, whichare not illustrated in FIG. 2 for clarity of the description.

The malware detector 210, which may be embodied as hardware, firmware,software, virtualized hardware, emulated architecture, and/or acombination thereof as discussed above, is configured to detect malwareby filtering for branches that satisfy the branch filters 202 and toanalyze those branches in more detail, such as by comparing them to themalware signatures 204, using a callback function. To do so, in theillustrative embodiment, the malware detector 210 includes a branchscanner 212 and a branch analyzer 214. The branch scanner 212, in theillustrative embodiment, is configured to provide filter criteria (i.e.,the branch filters 202) to the hook logic 114 and provide, to the hooklogic 114, an address of a callback function to be executed in responseto a determination that a monitored branch instruction satisfies thefilter criteria. To provide the branch filters 202 and the address ofthe callback function to the hook logic 114, the branch scanner 212 mayexecute on or more functions in a library, such as a dynamic linklibrary (DLL), to interface with the hook logic 114. The branch analyzer214, in the illustrative embodiment, is configured to execute thecallback function to analyze a branch instruction of an application thatsatisfied the one or more branch filters 202. In doing so, the branchanalyzer 214 may identify parameters of the branch instruction, whichmay be located in a stack in the memory 116, and compare the parametersto the malware signatures 204 to determine whether a match exists. Theparameters may be indicative of a write operation to write data toparticular files or memory regions, such as to embed a virus or othermalicious code, to read sensitive data from memory, or to perform otheroperations that may adversely affect the operation of the computingdevice 110.

It should be appreciated that each of the branch scanner 212 and thebranch analyzer 214 may be separately embodied as hardware, firmware,software, virtualized hardware, emulated architecture, and/or acombination thereof. For example, the branch scanner 212 may be embodiedas a hardware component, while the branch analyzer 214 is embodied as avirtualized hardware component or as some other combination of hardware,firmware, software, virtualized hardware, emulated architecture, and/ora combination thereof.

The storage manager 220, which may be embodied as hardware, firmware,software, virtualized hardware, emulated architecture, and/or acombination thereof as discussed above, is configured to maintain anunmodified system image (e.g., the unmodified image 206) and redirectdata writes to a shadow area (i.e., the shadow data 208) that isseparate from the unmodified image 206. Further, in the illustrativeembodiment, the storage manager 220 is configured to facilitate arecovery operation to return the computing device 110 to its earlierstate by reading the unmodified image 206 such as during a bootoperation. To do so, in the illustrative embodiment, the storage manager220 includes an image recoveror 222 and a data redirector 224. The imagerecoveror 222, in the illustrative embodiment, is configured tointerface with the checkpoint logic 122 of the data storage device 120to read the unmodified image 206 and restore the computing device 110 toits earlier state. In the illustrative embodiment, the image recoveror222 is configured to perform this operation during a boot operation in aUnified Extensible Firmware Interface (UEFI) environment. The dataredirector 224, in the illustrative embodiment, is configured to issue arequest to the firmware of the data storage device 120, and morespecifically, to firmware of the checkpoint logic 122, to enableredirection of data write requests to the shadow area (i.e., the shadowdata 208). In the illustrative embodiment, the data redirector 224enables this redirection during the boot operation, in the UEFIenvironment. In addition to the above functions, the storage manager220, in the illustrative embodiment, is configured to compare the shadowarea (i.e., the shadow data 208) to the unmodified image 206, such asafter malware has been detected, to determine what write operations wereperformed while the operating system was running. This information maythen be applied to the malware signatures 204 to provide furtherinformation on the activity of malware.

It should be appreciated that each of the image recoveror 222 and thedata redirector 224 may be separately embodied as hardware, firmware,software, virtualized hardware, emulated architecture, and/or acombination thereof. For example, the image recoveror 222 may beembodied as a hardware component, while the data redirector 224 isembodied as a virtualized hardware component or as some othercombination of hardware, firmware, software, virtualized hardware,emulated architecture, and/or a combination thereof.

The network communicator 230, which may be embodied as hardware,firmware, software, virtualized hardware, emulated architecture, and/ora combination thereof as discussed above, is configured to manageinbound and outbound data communications to and from the computingdevice 110, respectively. For example, the network communicator 230 maybe configured to transmit a request to the server 150 for an applicationand receive the application from the server 150 in response to therequest. The network communicator 230 may also be configured to transmitor receive other data, such as updated branch filters 202, updatedmalware signatures 204, and/or malware detection results, with anothercomputing device. In some embodiments, managing inbound and outbounddata communications to and from the computing device 110 may beperformed by one or more specialized hardware components, such as thenetwork interface controller (NIC) 128.

Referring now to FIG. 3, in use, the computing device 110 may execute amethod 300 for configuring the computing device 110 to detect malware.The method 300 begins with block 302, in which the computing device 110determines whether to perform the configuration process. In theillustrative embodiment, the computing device 110 may determine toperform the configuration process in response to an input from a user,such as the press of a corresponding button on the computing device 110or a selection in a graphical user interface. In other embodiments, thecomputing device 110 may be configured to perform the configurationprocess on a predefined schedule. In other embodiments, the computingdevice 110 may determine whether to perform the configuration processbased on other factors. Regardless, if the computing device 110determines to perform the configuration process, the method 300 advancesto block 304. In block 304, the computing device 110 boots the UEFIenvironment. The UEFI provides a software interface to firmware of thecomputing device 110, including the firmware of the checkpoint logic122. Subsequently, in block 306, the computing device 110 initializes ahardware assisted image recovery and data redirection. In doing so, asindicated in block 308, the computing device 110 recovers the unmodifiedsystem image (i.e., the unmodified image 206) from the data storagedevice 120. Further, as indicated in block 310, the computing device 110enables data write redirection to the shadow area (i.e., the shadow data208). As indicated in block 312, in the illustrative embodiment, thecomputing device 110 performs the recovery and the enablement of thedata write redirection atomically. Doing so reduces the likelihood thatany data writes could be made to the system image (i.e., the unmodifiedimage 206) before the data writes are redirected to the shadow area(i.e., the shadow data 208). As indicated in block 314, in theillustrative embodiment, to initiate the recovery and data redirection,the computing device 110 issues recovery and redirection requests fromthe UEFI environment to firmware of the data storage device 120, such asto firmware that enables interaction with the checkpoint logic 122. Inblock 316, the computing device 110 boots the operating system from therecovered system image (i.e., the unmodified image 206).

After the operating system has been booted, the method 300 advances toblock 318 of FIG. 4. In block 318, the computing device 110 sets branchfilter criteria, which defines parameters or other characteristics that,if present in a branch instruction, may be indicative of malware. Indoing so, the computing device 110 sets the branch filter criteria usinga library (e.g., a DLL) to interface with the hook logic 114 of the CPU.In the illustrative embodiment, the computing device 110 sets the branchfilter criteria to target system API calls, as indicated in block 322.As indicated in block 324, the computing device 110 sets an inline hookwithout recompiling the code of any applications that are to bemonitored. In other words, by setting the branch filter criteria, thecomputing device 110 will inspect all branch instructions that are to beexecuted and, for the branch instructions that match the criteria, thecomputing device 110 will execute a callback function to analyze thebranch instruction in more detail. In block 326, the computing device110 provides the address of the callback function to the hook logic 114of the CPU 112. Once the computing device 110 has performed theconfiguration method 300, the computing device 110 may proceed withmonitoring operations to detect malware, as described with reference toFIGS. 5 and 6.

Referring now to FIG. 5, in use, the computing device 110 may execute amethod 500 for configuring the computing device 110 to detect malware.The method 500 begins with block 502, in which the computing device 110determines whether to monitor for malware. In the illustrativeembodiment, the computing device 110 determines to proceed withmonitoring for malware if the computing device 110 has been configuredpursuant to the method 300, described above. In some embodiments, thecomputing device 110 additionally awaits a specific instruction toproceed with monitoring for malware, such as through a graphical userinterface or from another source. Regardless, if the computing device110 determines to monitor for malware, the method 500 advances to block504. In block 504, the computing device 110 executes one or moreapplications. The applications may be applications specifically selectedby a user or malware detection program for monitoring, or may be any andall applications that may be executed during the normal course of usingthe computing device 110. As described above, the hook logic 114provides the ability to efficiently monitor all instructions from allexecuted applications for specific criteria (i.e., branch filters).Accordingly, in the illustrative embodiment, it is not necessary tolimit the monitoring to only those applications already suspected toinclude malware.

In block 506, the computing device 110 monitors branch instructionsusing the hook logic 114 of the CPU 112 and compares the branchinstructions to the branch filter criteria (i.e., the branch filters202). In block 508, the computing device 110 determines whether amonitored branch instruction matches the branch filters 202. If not, themethod 500 loops back to block 504 in which the computing device 110continues to execute the applications. Otherwise, the method 500advances to block 510, in which the computing device 110 executes thecallback function at the address provided in block 326 of the method300. In block 512, while executing the callback function, the computingdevice 110 analyzes the branch instruction to detect malware. In doingso, the computing device 110 analyzes the context of the branchinstruction, as indicated in block 514. In the illustrative embodiment,in analyzing the context of the branch instruction, the computing device110 reads parameters associated with the branch instruction from thestack. The parameters may specify a file or memory region to write to,the data to be written, and/or other information. Further, in analyzingthe branch instruction to detect malware, the computing device 110compares the branch instruction to malware signature data (i.e., themalware signatures 204). As described above, the malware signatures 204are indicative of known behavior of malware, including malicious data orinstructions that malware is known to write and where the malware writesthe data (e.g., to a specific operating system file, etc.). In block520, the computing device 110 determines whether malware was detected inthe analysis of block 512. If not, the method 500 loops back to block504 in which the computing device 110 continues to execute applications.Otherwise, the method 500 advances to block 522 of FIG. 6, in which thecomputing device 110 displays the results of the analysis.

Referring now to FIG. 6, after the computing device 110 has determinedthat malware was detected, the computing device displays the results ofthe analysis, as indicated in block 522. In doing so, in theillustrative embodiment, the computing device 110 identifies differencesbetween the unmodified system image (i.e., the unmodified image 206) andthe shadow area (i.e., the shadow data 208), as indicated in block 524.This operation may be accelerated by the checkpoint logic 122 of thedata storage device 120. Further, in the illustrative embodiment, thecomputing device 110 determines the activity of the detected malwarefrom the identified differences between the unmodified image 206 and theshadow data 208. The differences may indicate data that was written bythe malware before the computing device 110 detected the malware (i.e.,earlier instructions that preceded the branch instruction that thecomputing device 110 determined matched a malware signature), asindicated in block 526. Additionally, as indicated in block 528, thecomputing device 110 may update the malware signature data (i.e., themalware signatures 204) based on the determined activity from block 526.Accordingly, the computing device 110 may more readily identify themalware in the future. In block 530, the computing device 110 restartsto perform the method 300 described with reference to FIGS. 3-4.

EXAMPLES

Illustrative examples of the technologies disclosed herein are providedbelow. An embodiment of the technologies may include any one or more,and any combination of, the examples described below.

Example 1 includes a computing device to perform hardware assistedmalware detection on an application, the computing device comprising oneor more processors with hook logic to monitor for execution of branchinstructions of the application, compare the monitored branchinstructions to filter criteria, and determine whether a monitoredbranch instruction satisfies the filter criteria; and a malware detectorto provide the filter criteria to the hook logic, provide an address ofa callback function to the hook logic to be executed in response to adetermination that a monitored branch instruction satisfies the filtercriteria, and analyze, in response to execution of the callbackfunction, the monitored branch instruction to determine whether themonitored branch instruction is indicative of malware.

Example 2 includes the subject matter of Example 1, and furtherincluding a storage manager to maintain an unmodified system image andredirect data writes to a shadow area that is separate from theunmodified system image.

Example 3 includes the subject matter of any of Examples 1 and 2, andfurther including a storage manager to atomically (i) perform a systemdata recovery with an unmodified system image and (ii) enableredirection of data writes to a shadow area that is separate from theunmodified system image.

Example 4 includes the subject matter of any of Examples 1-3, andwherein the storage manager is to perform the system data recovery andenablement of redirection of data writes in a unified extensiblefirmware interface (UEFI) environment before an operating system of thecomputing device is booted.

Example 5 includes the subject matter of any of Examples 1-4, andwherein to provide the filter criteria comprises to provide filtercriteria to target a call to an application programming interface (API)function.

Example 6 includes the subject matter of any of Examples 1-5, andwherein to analyze the branch instruction comprises to analyze one ormore parameters associated with the branch instruction.

Example 7 includes the subject matter of any of Examples 1-6, andwherein to analyze the one or more parameters comprises to read the oneor more parameters from a stack.

Example 8 includes the subject matter of any of Examples 1-7, andwherein the malware detector is further to restart, in response to adetermination that the monitored branch instruction is indicative ofmalware, the computing device.

Example 9 includes the subject matter of any of Examples 1-8, andwherein to analyze the branch instruction comprises to compare a contextof the monitored branch instruction to malware signature data indicativeof actions known to be performed by malware.

Example 10 includes the subject matter of any of Examples 1-9, andfurther including a storage manager to maintain an unmodified systemimage in a first area of a data storage device, selectively redirectdata writes to a shadow area that is separate from the first area, anddetermine, in response to a determination that a monitored branchinstruction is indicative of malware, differences between the shadowarea and the unmodified system image to identify malware behavior.

Example 11 includes the subject matter of any of Examples 1-10, andwherein the malware detector is further to add the identified malwarebehavior to a set of malware signature data.

Example 12 includes the subject matter of any of Examples 1-11, andfurther including a storage manager to atomically (i) perform a systemdata recovery with an unmodified system image stored in a solid statedrive and (ii) issue a request to firmware of the solid state drive toenable redirection of data writes to a shadow area that is separate fromthe unmodified system image.

Example 13 includes the subject matter of any of Examples 1-12, andwherein to provide the filter criteria to the hook logic comprises toissue a request to a library to pass the filter criteria to the hooklogic.

Example 14 includes the subject matter of any of Examples 1-13, andwherein the malware detector is further to use the hook logic to set aninline hook to the callback function without recompilation ofapplication code.

Example 15 includes a method for performing hardware assisted malwaredetection on an application, the method comprising providing, by acomputing device, filter criteria to hook logic of the computing device;providing, by the computing device, an address of a callback function tothe hook logic to be executed in response to a determination that amonitored branch instruction of the application satisfies the filtercriteria; monitoring, by the hook logic of the computing device,execution of branch instructions of the application; comparing, by thehook logic of the computing device, the monitored branch instructions tothe filter criteria; determining, by the hook logic of the computingdevice, whether one of the monitored branch instructions satisfies thefilter criteria; and executing, by the computing device, the callbackfunction in response to a determination that one of the monitored branchinstructions satisfies the filter criteria, to analyze the monitoredbranch instruction to determine whether the monitored branch instructionis indicative of malware.

Example 16 includes the subject matter of Example 15, and furtherincluding maintaining, by the computing device, an unmodified systemimage; and redirecting, by the computing device, data writes to a shadowarea that is separate from the unmodified system image.

Example 17 includes the subject matter of any of Examples 15 and 16, andfurther including atomically performing a system data recovery with anunmodified system image and enabling redirection of data writes to ashadow area that is separate from the unmodified system image.

Example 18 includes the subject matter of any of Examples 15-17, andwherein performing the system data recovery and enabling redirection ofdata writes comprises performing the system data recovery and enablingredirection of data writes in a unified extensible firmware interface(UEFI) environment before an operating system of the computing device isbooted.

Example 19 includes the subject matter of any of Examples 15-18, andwherein providing the filter criteria comprises providing filtercriteria to target a call to an application programming interface (API)function.

Example 20 includes the subject matter of any of Examples 15-19, andwherein analyzing the branch instruction comprises analyzing one or moreparameters associated with the branch instruction.

Example 21 includes the subject matter of any of Examples 15-20, andwherein analyzing the one or more parameters comprises reading the oneor more parameters from a stack.

Example 22 includes the subject matter of any of Examples 15-21, andfurther including restarting, in response to a determination that themonitored branch instruction is indicative of malware, the computingdevice.

Example 23 includes the subject matter of any of Examples 15-22, andwherein analyzing the branch instruction comprises comparing a contextof the monitored branch instruction to malware signature data indicativeof actions known to be performed by malware.

Example 24 includes the subject matter of any of Examples 15-23, andfurther including maintaining, by the computing device, an unmodifiedsystem image in a first area of a data storage device; selectivelyredirecting, by the computing device, data writes to a shadow area thatis separate from the first area; and determining, by the computingdevice and in response to a determination that a monitored branchinstruction is indicative of malware, differences between the shadowarea and the unmodified system image to identify malware behavior.

Example 25 includes the subject matter of any of Examples 15-24, andfurther including adding the identified malware behavior to a set ofmalware signature data.

Example 26 includes the subject matter of any of Examples 15-25, andfurther including atomically performing a system data recovery with anunmodified system image stored in a solid state drive and issuing arequest to firmware of the solid state drive to enable redirection ofdata writes to a shadow area that is separate from the unmodified systemimage.

Example 27 includes the subject matter of any of Examples 15-26, andwherein providing the filter criteria to the hook logic comprisesissuing a request to a library to pass the filter criteria to the hooklogic.

Example 28 includes the subject matter of any of Examples 15-27, andfurther including using the hook logic to set an inline hook to thecallback function without recompilation of application code.

Example 29 includes one or more machine-readable storage mediacomprising a plurality of instructions stored thereon that in responseto being executed, cause a computing device to perform the method of anyof Examples 15-28.

Example 30 includes a computing device comprising means for providingfilter criteria to hook logic of the computing device; means forproviding an address of a callback function to the hook logic to beexecuted in response to a determination that a monitored branchinstruction of the application satisfies the filter criteria; means formonitoring, with the hook logic, execution of branch instructions of theapplication; means for comparing, with the hook logic, the monitoredbranch instructions to the filter criteria; means for determining, withthe hook logic, whether one of the monitored branch instructionssatisfies the filter criteria; and means for executing the callbackfunction in response to a determination that one of the monitored branchinstructions satisfies the filter criteria, to analyze the monitoredbranch instruction to determine whether the monitored branch instructionis indicative of malware.

Example 31 includes the subject matter of Example 30, and furtherincluding means for maintaining an unmodified system image; and meansfor redirecting data writes to a shadow area that is separate from theunmodified system image.

Example 32 includes the subject matter of any of Examples 30 and 31, andfurther including means for atomically performing a system data recoverywith an unmodified system image and enabling redirection of data writesto a shadow area that is separate from the unmodified system image.

Example 33 includes the subject matter of any of Examples 30-32, andwherein the means for performing the system data recovery and enablingredirection of data writes comprises means for performing the systemdata recovery and enabling redirection of data writes in a unifiedextensible firmware interface (UEFI) environment before an operatingsystem of the computing device is booted.

Example 34 includes the subject matter of any of Examples 30-33, andwherein the means for providing the filter criteria comprises means forproviding filter criteria to target a call to an application programminginterface (API) function.

Example 35 includes the subject matter of any of Examples 30-34, andwherein the means for analyzing the branch instruction comprises meansfor analyzing one or more parameters associated with the branchinstruction.

Example 36 includes the subject matter of any of Examples 30-35, andwherein the means for analyzing the one or more parameters comprisesmeans for reading the one or more parameters from a stack.

Example 37 includes the subject matter of any of Examples 30-36, andfurther including means for restarting, in response to a determinationthat the monitored branch instruction is indicative of malware, thecomputing device.

Example 38 includes the subject matter of any of Examples 30-37, andwherein the means for analyzing the branch instruction comprises meansfor comparing a context of the monitored branch instruction to malwaresignature data indicative of actions known to be performed by malware.

Example 39 includes the subject matter of any of Examples 30-38, andfurther including means for maintaining an unmodified system image in afirst area of a data storage device; means for selectively redirectingdata writes to a shadow area that is separate from the first area; andmeans for determining, in response to a determination that a monitoredbranch instruction is indicative of malware, differences between theshadow area and the unmodified system image to identify malwarebehavior.

Example 40 includes the subject matter of any of Examples 30-39, andfurther including means for adding the identified malware behavior to aset of malware signature data.

Example 41 includes the subject matter of any of Examples 30-40, andfurther including means for atomically performing a system data recoverywith an unmodified system image stored in a solid state drive andissuing a request to firmware of the solid state drive to enableredirection of data writes to a shadow area that is separate from theunmodified system image.

Example 42 includes the subject matter of any of Examples 30-41, andwherein the means for providing the filter criteria to the hook logiccomprises means for issuing a request to a library to pass the filtercriteria to the hook logic.

Example 43 includes the subject matter of any of Examples 30-42, andfurther including means for using the hook logic to set an inline hookto the callback function without recompilation of application code.

1. A computing device to perform hardware assisted malware detection onan application, the computing device comprising: one or more processorswith hook logic to monitor for execution of branch instructions of theapplication, compare the monitored branch instructions to filtercriteria, and determine whether a monitored branch instruction satisfiesthe filter criteria; and a malware detector to provide the filtercriteria to the hook logic, provide an address of a callback function tothe hook logic to be executed in response to a determination that amonitored branch instruction satisfies the filter criteria, and analyze,in response to execution of the callback function, the monitored branchinstruction to determine whether the monitored branch instruction isindicative of malware.
 2. The computing device of claim 1, furthercomprising a storage manager to maintain an unmodified system image andredirect data writes to a shadow area that is separate from theunmodified system image.
 3. The computing device of claim 1, furthercomprising a storage manager to atomically (i) perform a system datarecovery with an unmodified system image and (ii) enable redirection ofdata writes to a shadow area that is separate from the unmodified systemimage.
 4. The computing device of claim 3, wherein the storage manageris to perform the system data recovery and enablement of redirection ofdata writes in a unified extensible firmware interface (UEFI)environment before an operating system of the computing device isbooted.
 5. The computing device of claim 1, wherein to provide thefilter criteria comprises to provide filter criteria to target a call toan application programming interface (API) function.
 6. The computingdevice of claim 1, wherein to analyze the branch instruction comprisesto analyze one or more parameters associated with the branchinstruction.
 7. The computing device of claim 6, wherein to analyze theone or more parameters comprises to read the one or more parameters froma stack.
 8. The computing device of claim 1, wherein the malwaredetector is further to restart, in response to a determination that themonitored branch instruction is indicative of malware, the computingdevice.
 9. The computing device of claim 1, wherein to analyze thebranch instruction comprises to compare a context of the monitoredbranch instruction to malware signature data indicative of actions knownto be performed by malware.
 10. The computing device of claim 1, furthercomprising a storage manager to maintain an unmodified system image in afirst area of a data storage device, selectively redirect data writes toa shadow area that is separate from the first area, and determine, inresponse to a determination that a monitored branch instruction isindicative of malware, differences between the shadow area and theunmodified system image to identify malware behavior.
 11. One or moremachine-readable storage media comprising a plurality of instructionsstored thereon that in response to being executed, cause a computingdevice to: provide filter criteria to hook logic of the computingdevice; provide an address of a callback function to the hook logic tobe executed in response to a determination that a monitored branchinstruction of the application satisfies the filter criteria; monitor,with the hook logic, execution of branch instructions of theapplication; compare, with the hook logic, the monitored branchinstructions to the filter criteria; determine, with the hook logic,whether one of the monitored branch instructions satisfies the filtercriteria; and execute the callback function in response to adetermination that one of the monitored branch instructions satisfiesthe filter criteria, to analyze the monitored branch instruction todetermine whether the monitored branch instruction is indicative ofmalware.
 12. The one or more machine-readable storage media of claim 11,wherein the plurality of instructions further cause the computing deviceto: maintain an unmodified system image; and redirect data writes to ashadow area that is separate from the unmodified system image.
 13. Theone or more machine-readable storage media of claim 11, wherein theplurality of instructions further cause the computing device toatomically perform a system data recovery with an unmodified systemimage and enable redirection of data writes to a shadow area that isseparate from the unmodified system image.
 14. The one or moremachine-readable storage media of claim 13, wherein to perform thesystem data recovery and enable redirection of data writes comprises toperform the system data recovery and enable redirection of data writesin a unified extensible firmware interface (UEFI) environment before anoperating system of the computing device is booted.
 15. The one or moremachine-readable storage media of claim 11, wherein to provide thefilter criteria comprises to provide filter criteria to target a call toan application programming interface (API) function.
 16. The one or moremachine-readable storage media of claim 11, wherein to analyze thebranch instruction comprises to analyze one or more parametersassociated with the branch instruction.
 17. The one or moremachine-readable storage media of claim 16, wherein to analyze the oneor more parameters comprises to read the one or more parameters from astack.
 18. The one or more machine-readable storage media of claim 11,wherein the plurality of instructions further cause the computing deviceto restart, in response to a determination that the monitored branchinstruction is indicative of malware, the computing device.
 19. The oneor more machine-readable storage media of claim 11, wherein to analyzethe branch instruction comprises to compare a context of the monitoredbranch instruction to malware signature data indicative of actions knownto be performed by malware.
 20. The one or more machine-readable storagemedia of claim 11, wherein the plurality of instructions further causethe computing device to: maintain an unmodified system image in a firstarea of a data storage device; selectively redirect data writes to ashadow area that is separate from the first area; and determine, inresponse to a determination that a monitored branch instruction isindicative of malware, differences between the shadow area and theunmodified system image to identify malware behavior.
 21. A method forperforming hardware assisted malware detection on an application, themethod comprising: providing, by a computing device, filter criteria tohook logic of the computing device; providing, by the computing device,an address of a callback function to the hook logic to be executed inresponse to a determination that a monitored branch instruction of theapplication satisfies the filter criteria; monitoring, by the hook logicof the computing device, execution of branch instructions of theapplication; comparing, by the hook logic of the computing device, themonitored branch instructions to the filter criteria; determining, bythe hook logic of the computing device, whether one of the monitoredbranch instructions satisfies the filter criteria; and executing, by thecomputing device, the callback function in response to a determinationthat one of the monitored branch instructions satisfies the filtercriteria, to analyze the monitored branch instruction to determinewhether the monitored branch instruction is indicative of malware. 22.The method of claim 21, further comprising: maintaining, by thecomputing device, an unmodified system image; and redirecting, by thecomputing device, data writes to a shadow area that is separate from theunmodified system image.
 23. The method of claim 21, further comprisingatomically performing a system data recovery with an unmodified systemimage and enabling redirection of data writes to a shadow area that isseparate from the unmodified system image.
 24. The method of claim 23,wherein performing the system data recovery and enabling redirection ofdata writes comprises performing the system data recovery and enablingredirection of data writes in a unified extensible firmware interface(UEFI) environment before an operating system of the computing device isbooted.
 25. The method of claim 21, wherein providing the filtercriteria comprises providing filter criteria to target a call to anapplication programming interface (API) function.